Dominance or Fair Play in Social Networks? A Model of Influencer Popularity Dynamic

TL;DR

This paper introduces a mean-field analytical model for influencer popularity dynamics in social networks, considering individual activity, content virality, external events, and platform effects, to predict influence distribution and fairness.

Contribution

It presents a novel, data-driven mean-field model that integrates multiple factors affecting influencer success and derives conditions for system ergodicity and influence fairness.

Findings

Conditions for system ergodicity are analytically derived.

Sensitivity analysis reveals factors promoting dominance or fair play.

Insights into social network evolution towards equity or bias.

Abstract

This paper presents a data-driven mean-field approach to model the popularity dynamics of users seeking public attention, i.e., influencers. We propose a novel analytical model that integrates individual activity patterns, expertise in producing viral content, exogenous events, and the platform's role in visibility enhancement, ultimately determining each influencer's success. We analytically derive sufficient conditions for system ergodicity, enabling predictions of popularity distributions. A sensitivity analysis explores various system configurations, highlighting conditions favoring either dominance or fair play among influencers. Our findings offer valuable insights into the potential evolution of social networks towards more equitable or biased influence ecosystems.